基于高斯过程连续速度积分的地面优化4D雷达惯性里程计
Ground-Optimized 4D Radar-Inertial Odometry via Continuous Velocity Integration using Gaussian Process
AI总结：
针对雷达惯性里程计中地面点噪声和异步数据流问题，提出地面优化噪声过滤与基于高斯过程的连续速度预积分方法，实现6自由度运动估计，垂直漂移小于1%。
摘要：
雷达在恶劣天气条件下确保了鲁棒的感知能力，然而由于其固有的高噪声水平，仍存在挑战。现有的雷达里程计已通过诸如过滤虚假点、利用多普勒速度或与惯性测量集成等策略克服了这些挑战。本文提出了超越现有雷达惯性里程计的两项新改进：地面优化噪声过滤和连续速度预积分。尽管地面平面在激光雷达里程计中广泛使用，但雷达测量的不精确地面点分布导致朴素平面拟合失败。与激光雷达中的平面拟合不同，我们引入了一种专门为雷达设计的基于区域的不确定性感知地面建模。其次，我们注意到雷达速度测量可以更好地与IMU结合，以在雷达惯性里程计中实现更精确的预积分。现有方法通常通过使用离散化传播模型简化异步数据流的复杂性，从而忽略雷达与IMU之间的时间差异。针对这一问题，我们利用高斯过程并制定了一种连续预积分方法，将3自由度线性速度与IMU紧密集成，直接从原始测量中实现完整的6自由度运动。我们的方法在精心条件下于公开数据集上展示了卓越的性能（垂直漂移小于1%），显著提高了高程精度。代码将作为开源发布给社区：https://github.com/wooseongY/Go-RIO。
英文摘要：
Radar ensures robust sensing capabilities in adverse weather conditions, yet challenges remain due to its high inherent noise level. Existing radar odometry has overcome these challenges with strategies such as filtering spurious points, exploiting Doppler velocity, or integrating with inertial measurements. This paper presents two novel improvements beyond the existing radar-inertial odometry: ground-optimized noise filtering and continuous velocity preintegration. Despite the widespread use of ground planes in LiDAR odometry, imprecise ground point distributions of radar measurements cause naive plane fitting to fail. Unlike plane fitting in LiDAR, we introduce a zone-based uncertainty-aware ground modeling specifically designed for radar. Secondly, we note that radar velocity measurements can be better combined with IMU for a more accurate preintegration in radar-inertial odometry. Existing methods often ignore temporal discrepancies between radar and IMU by simplifying the complexities of asynchronous data streams with discretized propagation models. Tackling this issue, we leverage GP and formulate a continuous preintegration method for tightly integrating 3-DOF linear velocity with IMU, facilitating full 6-DOF motion directly from the raw measurements. Our approach demonstrates remarkable performance (less than 1% vertical drift) in public datasets with meticulous conditions, illustrating substantial improvement in elevation accuracy. The code will be released as open source for the community: https://github.com/wooseongY/Go-RIO.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
